class_docstr: The model definition.
code: def __call__(self, x_dict: _InputBatch) -> _LogitBatch:
      # Each feature is of shape f32[B, 1]
      x_tuple = tuple(x_dict[feature] for feature in _FEATURE_KEYS_XF)
      x_array = jnp.concatenate(x_tuple, axis=-1)  # shape: f32[B, 4]
      assert x_array.ndim == 2
      assert x_array.shape[1] == 4
      x = x_array
  
      x = nn.Dense(features=8)(x)
      x = nn.relu(x)
      x = nn.Dense(features=8)(x)
      x = nn.relu(x)
      x = nn.Dense(features=3)(x)
      x = nn.log_softmax(x, axis=-1)
      return x
  
imports: import functools
  from typing import Callable, Dict, List
  import absl
  from flax import linen
  from flax.metrics import tensorboard
  import jax
  from jax import numpy
  from jax.experimental import jax2tf
  import numpy
  import optax
  import tensorflow
  import tensorflow_transform
  from tfx import v1
  from tfx.examples.penguin import penguin_utils_base
label_desc: Use image node_id 1 for calling the _FlaxPenguinModel obj's underlying member method code with example usage: obj.__call__(x_dict) and returns: x